Tag Archives: scanning

Robots excel at carrying out specialized tasks in controlled environments, but put them in your average office and they’d be lost. Alphabet wants to change that by developing what they call the Everyday Robot, which could learn to help us out with our daily chores.

For a long time most robots were painstakingly hand-coded to carry out their functions, but since the deep learning revolution earlier this decade there’s been a growing effort to imbue them with AI that lets them learn new tasks through experience.

That’s led to some impressive breakthroughs, like a robotic hand nimble enough to solve a Rubik’s cube and a robotic arm that can accurately toss bananas across a room.

And it turns out Alphabet’s early-stage research and development division, Alphabet X, has also secretly been using similar machine learning techniques to develop robots adaptable enough to carry out a range of tasks in cluttered and unpredictable human environments like homes and offices.

The robots they’ve built combine a wheeled base with a single arm and a head full of sensors (including LIDAR) for 3D scanning, borrowed from Alphabet’s self-driving car division, Waymo.

At the minute, though, they’re largely restricted to sorting trash for recycling, project leader Hans Peter Brondmo writes in a blog post. While that might sound mundane, identifying different kinds of trash, grasping it, and moving it to the correct bin is still a difficult thing for a robot to do consistently. Some of the robots also have to navigate around the office to sort trash at various recycling stations.

Alphabet says even its human staff were getting it wrong 20 percent of the time, but after several months of training the robots have managed to get that down to 3.5 percent.

Every day, 30 robots toil away in what’s been dubbed the “playpen” sorting trash, and then every night thousands of virtual robots continue to practice in a simulation. This experience is then used to update the robots’ control algorithms each night. All the robots also share their experiences with the others through a process called collaborative learning.

The process isn’t flawless, though. Simonite notes that while the robots exhibit some uncannily smart behaviors, like stirring piles of rubbish to make it easier to grab specific items, they also frequently miss or fumble the objects they’re trying to grasp.

Nonetheless, the project’s leaders are happy with their progress so far. And the hope is that creating robots that are able to learn from little more than experience in complex environments like an office should be a first step towards general-purpose robots that can pick up a variety of useful skills to assist humans.

Taking that next step will be the major test of the project. So far there’s been limited evidence that experience gained by robots in one task can be transferred to learning another. That’s something the group hopes to demonstrate next year.

And it seems there may be more robot news coming out of Alphabet X soon. The group has several other robotics “moonshots” in the pipeline, built on technology and talent transferred over in 2016 from the remains of a broadly unsuccessful splurge on robotics startups by former Google executive Andy Rubin.

Whether this robotics renaissance at Alphabet will finally help robots break into our homes and offices remains to be seen, but with the resources they have at hand, they just may be able to make it happen.

For all of human history, the experience of looking at the world was roughly the same for everyone. But boundaries between the digital and physical are beginning to fade.

The world around us is gaining layer upon layer of digitized, virtually overlaid information—making it rich, meaningful, and interactive. As a result, our respective experiences of the same environment are becoming vastly different, personalized to our goals, dreams, and desires.

Welcome to Web 3.0, or the Spatial Web. In version 1.0, static documents and read-only interactions limited the internet to one-way exchanges. Web 2.0 provided quite an upgrade, introducing multimedia content, interactive web pages, and participatory social media. Yet, all this was still mediated by two-dimensional screens.

As a result, we will soon be able to superimpose digital information atop any physical surrounding—freeing our eyes from the tyranny of the screen, immersing us in smart environments, and making our world endlessly dynamic.

In the third post of our five-part series on augmented reality, we will explore the convergence of AR, AI, sensors, and blockchain and dive into the implications through a key use case in manufacturing.

It all begins with graphics processing units (GPUs)—electric circuits that perform rapid calculations to render images. (GPUs can be found in mobile phones, game consoles, and computers.)

However, because AR requires such extensive computing power, single GPUs will not suffice. Instead, blockchain can now enable distributed GPU processing power, and blockchains specifically dedicated to AR holographic processing are on the rise.

Next up, cameras and sensors will aggregate real-time data from any environment to seamlessly integrate physical and virtual worlds. Meanwhile, body-tracking sensors are critical for aligning a user’s self-rendering in AR with a virtually enhanced environment. Depth sensors then provide data for 3D spatial maps, while cameras absorb more surface-level, detailed visual input. In some cases, sensors might even collect biometric data, such as heart rate and brain activity, to incorporate health-related feedback in our everyday AR interfaces and personal recommendation engines.

The next step in the pipeline involves none other than AI. Processing enormous volumes of data instantaneously, embedded AI algorithms will power customized AR experiences in everything from artistic virtual overlays to personalized dietary annotations.

In retail, AIs will use your purchasing history, current closet inventory, and possibly even mood indicators to display digitally rendered items most suitable for your wardrobe, tailored to your measurements.

In healthcare, smart AR glasses will provide physicians with immediately accessible and maximally relevant information (parsed from the entirety of a patient’s medical records and current research) to aid in accurate diagnoses and treatments, freeing doctors to engage in the more human-centric tasks of establishing trust, educating patients and demonstrating empathy.

Image Credit: PHD Ventures.
Convergence in Manufacturing
One of the nearest-term use cases of AR is manufacturing, as large producers begin dedicating capital to enterprise AR headsets. And over the next ten years, AR will converge with AI, sensors, and blockchain to multiply manufacturer productivity and employee experience.

Already, the International Air Transport Association (IATA) — whose airlines supply 82 percent of air travel — recently implemented industrial tech company Atheer’s AR headsets in cargo management. And with barely any delay, IATA reported a whopping 30 percent improvement in cargo handling speed and no less than a 90 percent reduction in errors.

With similar success rates, Boeing brought Skylight’s smart AR glasses to the runway, now used in the manufacturing of hundreds of airplanes. Sure enough—the aerospace giant has now seen a 25 percent drop in production time and near-zero error rates.

Beyond cargo management and air travel, however, smart AR headsets will also enable on-the-job training without reducing the productivity of other workers or sacrificing hardware. Jaguar Land Rover, for instance, implemented Bosch’s Re’flekt One AR solution to gear technicians with “x-ray” vision: allowing them to visualize the insides of Range Rover Sport vehicles without removing any dashboards.

And as enterprise capabilities continue to soar, AIs will soon become the go-to experts, offering support to manufacturers in need of assembly assistance. Instant guidance and real-time feedback will dramatically reduce production downtime, boost overall output, and even help customers struggling with DIY assembly at home.

Perhaps one of the most profitable business opportunities, AR guidance through centralized AI systems will also serve to mitigate supply chain inefficiencies at extraordinary scale. Coordinating moving parts, eliminating the need for manned scanners at each checkpoint, and directing traffic within warehouses, joint AI-AR systems will vastly improve workflow while overseeing quality assurance.

After its initial implementation of AR “vision picking” in 2015, leading courier company DHL recently announced it would continue to use Google’s newest smart lens in warehouses across the world. Motivated by the initial group’s reported 15 percent jump in productivity, DHL’s decision is part of the logistics giant’s $300 million investment in new technologies.

And as direct-to-consumer e-commerce fundamentally transforms the retail sector, supply chain optimization will only grow increasingly vital. AR could very well prove the definitive step for gaining a competitive edge in delivery speeds.

As explained by Vital Enterprises CEO Ash Eldritch, “All these technologies that are coming together around artificial intelligence are going to augment the capabilities of the worker and that’s very powerful. I call it Augmented Intelligence. The idea is that you can take someone of a certain skill level and by augmenting them with artificial intelligence via augmented reality and the Internet of Things, you can elevate the skill level of that worker.”

Already, large producers like Goodyear, thyssenkrupp, and Johnson Controls are using the Microsoft HoloLens 2—priced at $3,500 per headset—for manufacturing and design purposes.

Perhaps the most heartening outcome of the AI-AR convergence is that, rather than replacing humans in manufacturing, AR is an ideal interface for human collaboration with AI. And as AI merges with human capital, prepare to see exponential improvements in productivity, professional training, and product quality.

(2) Convergence with Sensors
On the hardware front, these AI-AR systems will require a mass proliferation of sensors to detect the external environment and apply computer vision in AI decision-making.

To measure depth, for instance, some scanning depth sensors project a structured pattern of infrared light dots onto a scene, detecting and analyzing reflected light to generate 3D maps of the environment. Stereoscopic imaging, using two lenses, has also been commonly used for depth measurements. But leading technology like Microsoft’s HoloLens 2 and Intel’s RealSense 400-series camera implement a new method called “phased time-of-flight” (ToF).

In ToF sensing, the HoloLens 2 uses numerous lasers, each with 100 milliwatts (mW) of power, in quick bursts. The distance between nearby objects and the headset wearer is then measured by the amount of light in the return beam that has shifted from the original signal. Finally, the phase difference reveals the location of each object within the field of view, which enables accurate hand-tracking and surface reconstruction.

With a far lower computing power requirement, the phased ToF sensor is also more durable than stereoscopic sensing, which relies on the precise alignment of two prisms. The phased ToF sensor’s silicon base also makes it easily mass-produced, rendering the HoloLens 2 a far better candidate for widespread consumer adoption.

To apply inertial measurement—typically used in airplanes and spacecraft—the HoloLens 2 additionally uses a built-in accelerometer, gyroscope, and magnetometer. Further equipped with four “environment understanding cameras” that track head movements, the headset also uses a 2.4MP HD photographic video camera and ambient light sensor that work in concert to enable advanced computer vision.

For natural viewing experiences, sensor-supplied gaze tracking increasingly creates depth in digital displays. Nvidia’s work on Foveated AR Display, for instance, brings the primary foveal area into focus, while peripheral regions fall into a softer background— mimicking natural visual perception and concentrating computing power on the area that needs it most.

Gaze tracking sensors are also slated to grant users control over their (now immersive) screens without any hand gestures. Conducting simple visual cues, even staring at an object for more than three seconds, will activate commands instantaneously.

And our manufacturing example above is not the only one. Stacked convergence of blockchain, sensors, AI and AR will disrupt almost every major industry.

Take healthcare, for example, wherein biometric sensors will soon customize users’ AR experiences. Already, MIT Media Lab’s Deep Reality group has created an underwater VR relaxation experience that responds to real-time brain activity detected by a modified version of the Muse EEG. The experience even adapts to users’ biometric data, from heart rate to electro dermal activity (inputted from an Empatica E4 wristband).

Now rapidly dematerializing, sensors will converge with AR to improve physical-digital surface integration, intuitive hand and eye controls, and an increasingly personalized augmented world. Keep an eye on companies like MicroVision, now making tremendous leaps in sensor technology.

While I’ll be doing a deep dive into sensor applications across each industry in our next blog, it’s critical to first discuss how we might power sensor- and AI-driven augmented worlds.

(3) Convergence with Blockchain
Because AR requires much more compute power than typical 2D experiences, centralized GPUs and cloud computing systems are hard at work to provide the necessary infrastructure. Nonetheless, the workload is taxing and blockchain may prove the best solution.

A major player in this pursuit, Otoy aims to create the largest distributed GPU network in the world, called the Render Network RNDR. Built specifically on the Ethereum blockchain for holographic media, and undergoing Beta testing, this network is set to revolutionize AR deployment accessibility.

Alphabet Chairman Eric Schmidt (an investor in Otoy’s network), has even said, “I predicted that 90% of computing would eventually reside in the web based cloud… Otoy has created a remarkable technology which moves that last 10%—high-end graphics processing—entirely to the cloud. This is a disruptive and important achievement. In my view, it marks the tipping point where the web replaces the PC as the dominant computing platform of the future.”

Leveraging the crowd, RNDR allows anyone with a GPU to contribute their power to the network for a commission of up to $300 a month in RNDR tokens. These can then be redeemed in cash or used to create users’ own AR content.

In a double win, Otoy’s blockchain network and similar iterations not only allow designers to profit when not using their GPUs, but also democratize the experience for newer artists in the field.

And beyond these networks’ power suppliers, distributing GPU processing power will allow more manufacturing companies to access AR design tools and customize learning experiences. By further dispersing content creation across a broad network of individuals, blockchain also has the valuable potential to boost AR hardware investment across a number of industry beneficiaries.

On the consumer side, startups like Scanetchain are also entering the blockchain-AR space for a different reason. Allowing users to scan items with their smartphone, Scanetchain’s app provides access to a trove of information, from manufacturer and price, to origin and shipping details.

Based on NEM (a peer-to-peer cryptocurrency that implements a blockchain consensus algorithm), the app aims to make information far more accessible and, in the process, create a social network of purchasing behavior. Users earn tokens by watching ads, and all transactions are hashed into blocks and securely recorded.

The writing is on the wall—our future of brick-and-mortar retail will largely lean on blockchain to create the necessary digital links.

Final Thoughts
Integrating AI into AR creates an “auto-magical” manufacturing pipeline that will fundamentally transform the industry, cutting down on marginal costs, reducing inefficiencies and waste, and maximizing employee productivity.

Bolstering the AI-AR convergence, sensor technology is already blurring the boundaries between our augmented and physical worlds, soon to be near-undetectable. While intuitive hand and eye motions dictate commands in a hands-free interface, biometric data is poised to customize each AR experience to be far more in touch with our mental and physical health.

And underpinning it all, distributed computing power with blockchain networks like RNDR will democratize AR, boosting global consumer adoption at plummeting price points.

As AR soars in importance—whether in retail, manufacturing, entertainment, or beyond—the stacked convergence discussed above merits significant investment over the next decade. The augmented world is only just getting started.

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Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

Robots can land on the Moon and drive on Mars, but what about the places they can’t reach? Designed by engineers as NASA’s Jet Propulsion Laboratory in Pasadena, California, a four-limbed robot named LEMUR (Limbed Excursion Mechanical Utility Robot) can scale rock walls, gripping with hundreds of tiny fishhooks in each of its 16 fingers and using artificial intelligence to find its way around obstacles. In its last field test in Death Valley, California, in early 2019, LEMUR chose a route up a cliff, scanning the rock for ancient fossils from the sea that once filled the area.

The LEMUR project has since concluded, but it helped lead to a new generation of walking, climbing and crawling robots. In future missions to Mars or icy moons, robots with AI and climbing technology derived from LEMUR could discover similar signs of life. Those robots are being developed now, honing technology that may one day be part of future missions to distant worlds.

[ NASA ]

This video demonstrates the autonomous footstep planning developed by IHMC. Robots in this video are the Atlas humanoid robot (DRC version) and the NASA Valkyrie. The operator specifies a goal location in the world, which is modeled as planar regions using the robot’s perception sensors. The planner then automatically computes the necessary steps to reach the goal using a Weighted A* algorithm. The algorithm does not reject footholds that have a certain amount of support, but instead modifies them after the plan is found to try and increase that support area.

Currently, narrow terrain has a success rate of about 50%, rough terrain is about 90%, whereas flat ground is near 100%. We plan on increasing planner speed and the ability to plan through mazes and to unseen goals by including a body-path planner as the first step. Control, Perception, and Planning algorithms by IHMC Robotics.

[ IHMC ]

I’ve never really been able to get into watching people play poker, but throw an AI from CMU and Facebook into a game of no-limit Texas hold’em with five humans, and I’m there.

[ Facebook ]

In this video, Cassie Blue is navigating autonomously. Right now, her world is very small, the Wavefield at the University of Michigan, where she is told to turn left at intersections. You’re right, that is not a lot of independence, but it’s a first step away from a human and an RC controller!

Using a RealSense RGBD Camera, an IMU, and our version of an InEKF with contact factors, Cassie Blue is building a 3D semantic map in real time that identifies sidewalks, grass, poles, bicycles, and buildings. From the semantic map, occupancy and cost maps are built with the sidewalk identified as walk-able area and everything else considered as an obstacle. A planner then sets a goal to stay approximately 50 cm to the right of the sidewalk’s left edge and plans a path around obstacles and corners using D*. The path is translated into way-points that are achieved via Cassie Blue’s gait controller.

[ University of Michigan ]

Thanks Jesse!

Dave from HEBI Robotics wrote in to share some new actuators that are designed to get all kinds of dirty: “The R-Series takes HEBI’s X-Series to the next level, providing a sealed robotics solution for rugged, industrial applications and laying the groundwork for industrial users to address challenges that are not well met by traditional robotics. To prove it, we shot some video right in the Allegheny River here in Pittsburgh. Not a bad way to spend an afternoon :-)”

The R-Series Actuator is a full-featured robotic component as opposed to a simple servo motor. The output rotates continuously, requires no calibration or homing on boot-up, and contains a thru-bore for easy daisy-chaining of wiring. Modular in nature, R-Series Actuators can be used in everything from wheeled robots to collaborative robotic arms. They are sealed to IP67 and designed with a lightweight form factor for challenging field applications, and they’re packed with sensors that enable simultaneous control of position, velocity, and torque.

[ HEBI Robotics ]

Thanks Dave!

If your robot hands out karate chops on purpose, that’s great. If it hands out karate chops accidentally, maybe you should fix that.

COVR is short for “being safe around collaborative and versatile robots in shared spaces”. Our mission is to significantly reduce the complexity in safety certifying cobots. Increasing safety for collaborative robots enables new innovative applications, thus increasing production and job creation for companies utilizing the technology. Whether you’re an established company seeking to deploy cobots or an innovative startup with a prototype of a cobot related product, COVR will help you analyze, test and validate the safety for that application.

[ COVR ]

Thanks Anna!

EPFL startup Flybotix has developed a novel drone with just two propellers and an advanced stabilization system that allow it to fly for twice as long as conventional models. That fact, together with its small size, makes it perfect for inspecting hard-to-reach parts of industrial facilities such as ducts.

[ Flybotix ]

SpaceBok is a quadruped robot designed and built by a Swiss student team from ETH Zurich and ZHAW Zurich, currently being tested using Automation and Robotics Laboratories (ARL) facilities at our technical centre in the Netherlands. The robot is being used to investigate the potential of ‘dynamic walking’ and jumping to get around in low gravity environments.

SpaceBok could potentially go up to 2 m high in lunar gravity, although such a height poses new challenges. Once it comes off the ground the legged robot needs to stabilise itself to come down again safely – like a mini-spacecraft. So, like a spacecraft. SpaceBok uses a reaction wheel to control its orientation.

[ ESA ]

A new video from GITAI showing progress on their immersive telepresence robot for space.

[ GITAI ]

Tech United’s HERO robot (a Toyota HSR) competed in the RoboCup@Home competition, and it had a couple of garbage-related hiccups.

[ Tech United ]

Even small drones are getting better at autonomous obstacle avoidance in cluttered environments at useful speeds, as this work from the HKUST Aerial Robotics Group shows.

[ HKUST ]

DelFly Nimbles now come in swarms.

[ DelFly Nimble ]

This is a very short video, but it’s a fairly impressive look at a Baxter robot collaboratively helping someone put a shirt on, a useful task for folks with disabilities.

[ Shibata Lab ]

ANYmal can inspect the concrete in sewers for deterioration by sliding its feet along the ground.

[ ETH Zurich ]

HUG is a haptic user interface for teleoperating advanced robotic systems as the humanoid robot Justin or the assistive robotic system EDAN. With its lightweight robot arms, HUG can measure human movements and simultaneously display forces from the distant environment. In addition to such teleoperation applications, HUG serves as a research platform for virtual assembly simulations, rehabilitation, and training.

[ DLR ]

This video about “image understanding” from CMU in 1979 (!) is amazing, and even though it’s long, you won’t regret watching until 3:30. Or maybe you will.

[ ARGOS (pdf) ]

Will Burrard-Lucas’ BeetleCam turned 10 this month, and in this video, he recounts the history of his little robotic camera.

[ BeetleCam ]

In this week’s episode of Robots in Depth, Per speaks with Gabriel Skantze from Furhat Robotics.

Gabriel Skantze is co-founder and Chief Scientist at Furhat Robotics and Professor in speech technology at KTH with a specialization in conversational systems. He has a background in research into how humans use spoken communication to interact.

In this interview, Gabriel talks about how the social robot revolution makes it necessary to communicate with humans in a human ways through speech and facial expressions. This is necessary as we expand the number of people that interact with robots as well as the types of interaction. Gabriel gives us more insight into the many challenges of implementing spoken communication for co-bots, where robots and humans work closely together. They need to communicate about the world, the objects in it and how to handle them. We also get to hear how having an embodied system using the Furhat robot head helps the interaction between humans and the system.

The narrative that often accompanies most stories about artificial intelligence these days is how machines will disrupt any number of industries, from healthcare to transportation. It makes sense. After all, technology already drives many of the innovations in these sectors of the economy.

But sneakers and the red carpet? The definitively low-tech fashion industry would seem to be one of the last to turn over its creative direction to data scientists and machine learning algorithms.

However, big brands, e-commerce giants, and numerous startups are betting that AI can ingest data and spit out Chanel. Maybe it’s not surprising, given that fashion is partly about buzz and trends—and there’s nothing more buzzy and trendy in the world of tech today than AI.

In its annual survey of the $3 trillion fashion industry, consulting firm McKinsey predicted that while AI didn’t hit a “critical mass” in 2018, it would increasingly influence the business of everything from design to manufacturing.

“Fashion as an industry really has been so slow to understand its potential roles interwoven with technology. And, to be perfectly honest, the technology doesn’t take fashion seriously.” This comment comes from Zowie Broach, head of fashion at London’s Royal College of Arts, who as a self-described “old fashioned” designer has embraced the disruptive nature of technology—with some caveats.

Co-founder in the late 1990s of the avant-garde fashion label Boudicca, Broach has always seen tech as a tool for designers, even setting up a website for the company circa 1998, way before an online presence became, well, fashionable.

Broach told Singularity Hub that while she is generally optimistic about the future of technology in fashion—the designer has avidly been consuming old sci-fi novels over the last few years—there are still a lot of difficult questions to answer about the interface of algorithms, art, and apparel.

For instance, can AI do what the great designers of the past have done? Fashion was “about designing, it was about a narrative, it was about meaning, it was about expression,” according to Broach.

AI that designs products based on data gleaned from human behavior can potentially tap into the Pavlovian response in consumers in order to make money, Broach noted. But is that channeling creativity, or just digitally dabbling in basic human brain chemistry?

She is concerned about people retaining control of the process, whether we’re talking about their data or their designs. But being empowered with the insights machines could provide into, for example, the geographical nuances of fashion between Dubai, Moscow, and Toronto is thrilling.

“What is it that we want the future to be from a fashion, an identity, and design perspective?” she asked.

Off on the Right Foot
Silicon Valley and some of the biggest brands in the industry offer a few answers about where AI and fashion are headed (though not at the sort of depths that address Broach’s broader questions of aesthetics and ethics).

Take what is arguably the biggest brand in fashion, at least by market cap but probably not by the measure of appearances on Oscar night: Nike. The $100 billion shoe company just gobbled up an AI startup called Celect to bolster its data analytics and optimize its inventory. In other words, Nike hopes it will be able to figure out what’s hot and what’s not in a particular location to stock its stores more efficiently.

The company is going even further with Nike Fit, a foot-scanning platform using a smartphone camera that applies AI techniques from fields like computer vision and machine learning to find the best fit for each person’s foot. The algorithms then identify and recommend the appropriately sized and shaped shoe in different styles.

No doubt the next step will be to 3D print personalized and on-demand sneakers at any store.

San Francisco-based startup ThirdLove is trying to bring a similar approach to bra sizes. Its 20-member data team, Fortune reported, has developed the Fit Finder quiz that uses machine learning algorithms to help pick just the right garment for every body type.

Data scientists are also a big part of the team at Stitch Fix, a former San Francisco startup that went public in 2017 and today sports a market cap of more than $2 billion. The online “personal styling” company uses hundreds of algorithms to not only make recommendations to customers, but to help design new styles and even manage the subscription-based supply chain.

One notable attempt involved a “styling assistant” that came with the company’s Echo Look camera that helped people catalog and manage their wardrobes, evening helping pick out each day’s attire. The company more recently revisited the direct consumer side of AI with an app called StyleSnap, which matches clothes and accessories uploaded to the site with the retailer’s vast inventory and recommends similar styles.

Behind the curtains, Amazon is going even further. A team of researchers in Israel have developed algorithms that can deduce whether a particular look is stylish based on a few labeled images. Another group at the company’s San Francisco research center was working on tech that could generate new designs of items based on images of a particular style the algorithms trained on.

“I will say that the accumulation of many new technologies across the industry could manifest in a highly specialized style assistant, far better than the examples we’ve seen today. However, the most likely thing is that the least sexy of the machine learning work will become the most impactful, and the public may never hear about it.”

That prediction is from an online interview with Leanne Luce, a fashion technology blogger and product manager at Google who recently wrote a book called, succinctly enough, Artificial Intelligence and Fashion.

Data Meets Design
Academics are also sticking their beakers into AI and fashion. Researchers at the University of California, San Diego, and Adobe Research have previously demonstrated that neural networks, a type of AI designed to mimic some aspects of the human brain, can be trained to generate (i.e., design) new product images to match a buyer’s preference, much like the team at Amazon.

Meanwhile, scientists at Hong Kong Polytechnic University are working with China’s answer to Amazon, Alibaba, on developing a FashionAI Dataset to help machines better understand fashion. The effort will focus on how algorithms approach certain building blocks of design, what are called “key points” such as neckline and waistline, and “fashion attributes” like collar types and skirt styles.

The man largely behind the university’s research team is Calvin Wong, a professor and associate head of Hong Kong Polytechnic University’s Institute of Textiles and Clothing. His group has also developed an “intelligent fabric defect detection system” called WiseEye for quality control, reducing the chance of producing substandard fabric by 90 percent.

Wong and company also recently inked an agreement with RCA to establish an AI-powered design laboratory, though the details of that venture have yet to be worked out, according to Broach.

One hope is that such collaborations will not just get at the technological challenges of using machines in creative endeavors like fashion, but will also address the more personal relationships humans have with their machines.

“I think who we are, and how we use AI in fashion, as our identity, is not a superficial skin. It’s very, very important for how we define our future,” Broach said.

Soft robots are getting more and more popular for some very good reasons. Their relative simplicity is one. Their relative low cost is another. And for their simplicity and low cost, they’re generally able to perform very impressively, leveraging the unique features inherent to their design and construction to move themselves and interact with their environment. The other significant reason why soft robots are so appealing is that they’re durable. Without the constraints of rigid parts, they can withstand the sort of abuse that would make any roboticist cringe.

In the current issue of Science Robotics, a group of researchers from Tsinghua University in China and University of California, Berkeley, present a new kind of soft robot that’s both higher performance and much more robust than just about anything we’ve seen before. The deceptively simple robot looks like a bent strip of paper, but it’s able to move at 20 body lengths per second and survive being stomped on by a human wearing tennis shoes. Take that, cockroaches.

This prototype robot measures just 3 centimeters by 1.5 cm. It takes a scanning electron microscope to actually see what the robot is made of—a thermoplastic layer is sandwiched by palladium-gold electrodes, bonded with adhesive silicone to a structural plastic at the bottom. When an AC voltage (as low as 8 volts but typically about 60 volts) is run through the electrodes, the thermoplastic extends and contracts, causing the robot’s back to flex and the little “foot” to shuffle. A complete step cycle takes just 50 milliseconds, yielding a 200 hertz gait. And technically, the robot “runs,” since it does have a brief aerial phase.

Image: Science Robotics

Photos from a high-speed camera show the robot’s gait (A to D) as it contracts and expands its body.

To put the robot’s top speed of 20 body lengths per second in perspective, have a look at this nifty chart, which shows where other animals relative running speeds of some animals and robots versus body mass:

Image: Science Robotics

This chart shows the relative running speeds of some mammals (purple area), arthropods (orange area), and soft robots (blue area) versus body mass. For both mammals and arthropods, relative speeds show a strong negative scaling law with respect to the body mass: speeds increase as body masses decrease. However, for soft robots, the relationship appears to be the opposite: speeds decrease as the body mass decrease. For the little soft robots created by the researchers from Tsinghua University and UC Berkeley (red stars), the scaling law is similar to that of living animals: Higher speed was attained as the body mass decreased.

If you were wondering, like we were, just what that number 39 is on that chart (top left corner), it’s a species of tiny mite that was discovered underneath a rock in California in 1916. The mite is just under 1 mm in size, but it can run at 0.8 kilometer per hour, which is 322 body lengths per second, making it by far (like, by a factor of two at least) the fastest land animal on Earth relative to size. If a human was to run that fast relative to our size, we’d be traveling at a little bit over 2,000 kilometers per hour. It’s not a coincidence that pretty much everything in the upper left of the chart is an insect—speed scales favorably with decreasing mass, since actuators have a proportionally larger effect.

Other notable robots on the chart with impressive speed to mass ratios are number 27, which is this magnetically driven quadruped robot from UMD, and number 86, UC Berkeley’s X2-VelociRoACH.

Anyway, back to this robot. Some other cool things about it:

You can step on it, squishing it flat with a load about 1 million times its own body weight, and it’ll keep on crawling, albeit only half as fast.
Even climbing a slope of 15 degrees, it can still manage to move at 1 body length per second.
It carries peanuts! With a payload of six times its own weight, it moves a sixth as fast, but still, it’s not like you need your peanuts delivered all that quickly anyway, do you?

Image: Science Robotics

The researchers also put together a prototype with two legs instead of one, which was able to demonstrate a potentially faster galloping gait by spending more time in the air. They suggest that robots like these could be used for “environmental exploration, structural inspection, information reconnaissance, and disaster relief,” which are the sorts of things that you suggest that your robot could be used for when you really have no idea what it could be used for. But this work is certainly impressive, with speed and robustness that are largely unmatched by other soft robots. An untethered version seems possible due to the relatively low voltages required to drive the robot, and if they can put some peanut-sized sensors on there as well, practical applications might actually be forthcoming sometime soon.